Classification of mammographic breast density and its correlation with BI-RADS in elder women using machine learning approach

IntroductionMammographic breast density (MBD) is a known risk factor for breast cancer and older women have higher incidence rates of breast cancer occurrence. The Breast Imaging Reporting and Data System (BI-RADS) is a commonly used MBD classification tool for mammogram reporting. However, they have limitations since there are reading inconsistencies between different radiologists with the visual assessment of breast density.MethodsDigitised film-screen mammographic images were extracted from the Digital Database for Screening Mammography (DDSM). A machine learning project was developed using commercially available software with several predictive models applied to classify different amount of MBD on mammograms into different density groups. The effectiveness of different predictive models used in classifying the mammograms were tested by receiver operator characteristics (ROC) curve with comparison to the gold standard of BI-RADS classification.ResultsThree predictive models, Decision Tree (Tree), Support Vector Model (SVM) and k-Nearest Neighbour (kNN) showed high AUC values of 0.801, 0.805 and 0.810 respectively. High AUC values for the three predictive models indicates that the accuracy of the model is approaching that of the BI-RADS method.DiscussionOur machine learning project showed to have capabilities to be potentially used in the clinical settings to help categorise mammograms into extremely dense breasts (BI-RADS Group A) from entirely fatty breasts (BI-RADS Group D).ConclusionFindings from the present study suggest that the machine learning method is potentially useful to quantify the amount of MBD in mammograms.     

Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment

Breast density is an important risk factor for breast cancer that also affects the specificity and sensitivity of screening mammography. Current federal legislation mandates reporting of breast density for all women undergoing breast cancer screening. Clinically, breast density is assessed visually using the American College of Radiology Breast Imaging Reporting And Data System (BI-RADS) scale. Here, we introduce an artificial intelligence (AI) method to estimate breast density from digital mammograms. Our method leverages deep learning using two convolutional neural network architectures to accurately segment the breast area. An AI algorithm combining superpixel generation and radiomic machine learning is then applied to differentiate dense from non-dense tissue regions within the breast, from which breast density is estimated. Our method was trained and validated on a multi-racial, multi-institutional dataset of 15,661 images (4,437 women), and then tested on an independent matched case-control dataset of 6368 digital mammograms (414 cases; 1178 controls) for both breast density estimation and case-control discrimination. On the independent dataset, breast percent density (PD) estimates from Deep-LIBRA and an expert reader were strongly correlated (Spearman correlation coefficient = 0.90). Moreover, in a model adjusted for age and BMI, Deep-LIBRA yielded a higher case-control discrimination performance (area under the ROC curve, AUC = 0.612 [95% confidence interval (CI): 0.584, 0.640]) compared to four other widely-used research and commercial breast density assessment methods (AUCs = 0.528 to 0.599). Our results suggest a strong agreement of breast density estimates between Deep-LIBRA and gold-standard assessment by an expert reader, as well as improved performance in breast cancer risk assessment over state-of-the-art open-source and commercial methods.     

数字化乳腺X线引导下钙化导丝定位切除活检病例的影像与病理的相关性分析

目的 回顾性分析数字化乳腺X线引导下钙化导丝定位切除活检证实的良恶性病例之间X线征象差异性,筛选乳腺良恶性微钙化鉴别的有效客观性影像学因子,分析导丝定位存在的问题,为获得更客观地活检指征、降低乳腺微钙化过度活检提供依据。材料与方法收集2009-7至2011-3年数字化乳腺X线引导下钙化导丝定位切除活检病例。阅片者在不知道钙化良恶性结果的情况下阅读X线图像,记录病变X线征象（钙化类型、分布类型、分布范围、背景腺体密度、BI-RADS）。比较良恶性微钙化的X线征象差异性,分析各乳腺X线征象对良恶性微钙化鉴别的意义。比较BI-RADS 4A、4B、4C组间良恶性微钙化的分布差异。结果 入组共98例,良性72例（73.5%）,恶性26例（26.5%）。经过分析,病变良恶性与钙化类型、BI-RADS分级相关,与年龄、钙化分布、分布范围及腺体密度无显著相关。BI-RADS 4级亚级间良恶性病变分布存在差异（P=0.003）,BI-RADS 4C的恶性病变所占比例最高（72.7%）,BI-RADS 4A的良性病例所占比例最高（84.6%）。结论 乳腺X线引导下导丝定位切除活检能够有效地发现乳腺癌,但尽量避免过度活检。钙化类型和BI-RADS是恶性乳腺钙化的有效影响因子。     

Reducing the semantic gap in content-based image retrieval in mammography with relevance feedback and inclusion of expert knowledge

Object

We investigate the use of relevance feedback (RFb) and the inclusion of expert knowledge to reduce the semantic gap in content-based image retrieval (CBIR) of mammograms.

Materials and methods

Tests were conducted with radiologists, in which their judgment of the relevance of the retrieved images was used with techniques of query-point movement to incorporate RFb. The measures of similarity of images used for CBIR were based upon textural characteristics and the distribution of density of fibroglandular tissue in the breast. The features used include statistics of the gray-level histogram, texture features based upon the gray-level co-occurrence matrix, moment-based features, measures computed in the Radon domain, and granulometric measures. Queries for CBIR with RFb were executed by three radiologists. The performance of CBIR was measured in terms of precision of retrieval and a measure of relevance-weighted precision (RWP) of retrieval.

Results

The results indicate improvement due to RFb of up to 62% in precision and 39% in RWP.

Conclusion

The gain in performance of CBIR with RFb depended upon the BI-RADS breast density index of the query mammographic image, with greater improvement in cases of mammograms with higher density.     

Simulation of mammographic breast compression in 3D MR images using ICP-based B-spline deformation for multimodality breast cancer diagnosis

Purpose

   Multimodality mammography using conventional 2D mammography and dynamic contrast-enhanced 3D magnetic resonance imaging (DCE-MRI) is frequently performed for breast cancer detection and diagnosis. Combination of both imaging modalities requires superimposition of corresponding structures in mammograms and MR images. This task is challenging due to large differences in (1) dimensionality and spatial resolution, (2) variations in tissue contrast, as well as (3) differences in breast orientation and deformation during the image acquisition. A new method for multimodality breast image registration was developed and tested.

Methods

   Combined diagnosis of mammograms and MRI datasets was achieved by simulation of mammographic breast compression to overcome large differences in breast deformation. Surface information was extracted from the 3D MR image, and back-projection of the 2D breast contour in the mammogram was done. B-spline-based 3D/3D surface-based registration was then used to approximate mammographic breast compression. This breast deformation simulation was performed on 14 MRI datasets with 19 corresponding mammograms. The results were evaluated by comparison with distances between corresponding structures identified by an expert observer.

Results

   The evaluation revealed an average distance of 6.46 mm between corresponding structures, when an optimized initial alignment between both image datasets is performed. Without the optimization, the accuracy is 9.12 mm.

Conclusion

   A new surface-based method that approximates the mammographic deformation due to breast compression without using a specific complex model needed for finite-element-based methods was developed and tested with favorable results. The simulated compression can serve as foundation for a point-to-line correspondence between 2D mammograms and 3D MR image data.     

The relationship between breast density and bone mineral density after menopause

[Purpose] The purpose of this study was to analyze the relationship between breast density and bone mineral density after menopause. [Subjects and Methods] The subjects were 130 patients who participated in a bone densitometry test and had a mammogram taken between January 1st, 2013 to October 1st, 2014. The mammograms were scored breast imaging-reporting and data system. Grade 1 indicates almost only fat, Grade 2 indicates fibroglandular densities, Grade 3 indicates heterogeneously dense tissue, and Grade 4 indicates an extreme density. Correlation analysis was carried out to investigate the relationship between breast density grades and bone mineral densities by age and body mass index. [Results] Breast density had a close relationship with age (−0.59), Body mass index (−0.39), and T-score (0.29). The results indicate that as age and body mass index increase, the grade of the breast density decreases, and as the T-score increases, the grade increases. [Conclusion] A precise evaluation of the of breast cancer risk associated with breast density should be conducted as a large scale prospective study for women in Korea.Key words: Breast density, Bone mineral density     

数字化乳腺X线引导下导丝定位钙化及切除活检患者的影像学表现与病理的相关性

目的 分析经数字化乳腺X线引导下导丝定位钙化切除活检证实的良恶性乳腺病变的X线征象,筛选有效客观影像学因子。方法 收集接受数字化乳腺X线引导下导丝定位钙化切除活检的乳腺病变患者98例,分析并记录病变的X线征象,比较良恶性病变X线征象差异及BI-RADS4类患者A、B、C亚类中良恶性病变构成比的差异。结果 98例中,良性病变72例（72/98,73.47%）,恶性26例（26/98,26.53%）。良恶性病变的钙化类型、BI-RADS分类差异有统计学意义（P均<0.05）;BI-RADS类患者3三个亚类中,良恶性病变构成比差异有统计学意义（P=0.003）,BI-RADS4C中恶性病变比例最高（8/11,72.73%）,BI-RADS4A中良性病变比例最高（22/26,84.62%）。结论 乳腺X线引导下导丝定位钙化切除活检术能够有效发现乳腺癌。钙化类型和BI-RADS分类是恶性乳腺钙化的有效影响因子。     

乳腺假血管瘤样间质增生的超声和X线表现

目的 观察乳腺假血管瘤样间质增生（PASH）超声和乳房X线片表现。方法 纳入51例经病理证实PASH 患者,共54个结节,观察其超声及X线表现。采用Newcombe法评价超声及X线结节检出率的差异。结果 54个乳腺PASH结节中,26个（26/54,48.15%）经超声评价为乳腺影像报告和数据系统（BI-RADS）3类、28个51.85%（28/54）为BI-RADS 4类;其中41个（41/54,75.93%）表现为低回声;31个为椭圆形（31/54,57.41%）;29个（29/54,53.70%）边缘不光整;52个（52/54,96.30%）结节内未见钙化;44个（44/54,81.48%）未见血流信号。30例接受X线检查,15例未见占位,其中3例仅显示腺体密度不对称;12例（12个结节）可见无钙化的高密度影;7个（7/12,58.33%）边缘清晰。超声及X线对乳腺PASH结节的检出率分别为100.00%（54/54）和40.00%（12/30）,95%CI为（26.69%,63.86%）（P<0.05）。结论 乳腺PASH超声及X线表现均缺乏特异性,但超声检出率显著高于X线。     

BI-RADS词典钙化描述语的应用价值分析

目的：依据乳腺影像报告和数据系统（breast imaging-report and data system,BI-RADS）词典中的钙化描述语,回顾性分析数字钼靶摄影中钙化病灶的恶性度。方法收集126例可疑恶性钙化的病例,由2位影像科医生对数字钼靶摄影中的钙化进行分析,从形态和分布两方面进行描述语记录,之后与术后病理结果对照,分析BI-RADS钙化描述语对恶性度的预测价值。结果126例病例中,恶性61例。形态描述语中,恶性度最高的是细线或细线分支状（93%）;分布描述语中：恶性度最高的是段样（75%）。结论 BI-RADS词典中,钙化描述语能够帮助评估数字钼靶摄影中钙化病灶的恶性度。     

Sonographic evaluation of mammographically detected microcalcifications without a mass prior to stereotactic core needle biopsy

PURPOSE: The purpose of this prospective study was to evaluate the clinical usefulness of sonographically re-evaluating areas of microcalcification found mammographically before undertaking stereotactic core needle biopsy (SCNB). METHODS: Patients with nonpalpable breast lesions appearing as microcalcifications on mammograms and who had been referred to us for SCNB were re-evaluated sonographically before the procedure. None of the breast lesions had been associated with a density on the mammograms, and the initial sonographic evaluations had been negative. Using the mammograms for correlation, we meticulously re-evaluated the areas of microcalcifications sonographically using a high-frequency linear-array transducer. The sonographic and histopathologic results were then reviewed and correlated. The sonographic findings and visibility of the mammographically detected microcalcifications were analyzed by the 2-tailed Fisher's exact test and the chi-square test. RESULTS: Sixty-six patients, who had 68 cases of microcalcifications, were enrolled. Thirteen of the 66 patients underwent surgery, and 9 of the 13 were found to have breast carcinoma. In the sonographic re-evaluation before SCNB in these 9 patients, an associated soft tissue mass was demonstrated in 5 patients but not in the other 4. Sonographic re-evaluation also revealed abnormalities in 24 of 68 cases (35.3%), in contrast to the negative findings on the initial sonography. Using the chi-square test to identify a trend, we found that the percentage of cases that were sonographically visible was highest for clustered benign microcalcifications and lowest for segmental benign microcalcifications (p < 0.0001). CONCLUSIONS: In breast lesions that appear as microcalcifications without an associated mass on mammograms, pre-SCNB sonographic re-evaluation with a high-frequency transducer can depict microcalcifications, particularly the clustered ones, and can detect small associated masses. Although the absence of a sonographically detectable mass in areas of mammographically detected microcalcifications does not guarantee the absence of cancer, the presence of an associated mass on sonography should warrant close follow-up in the case of negative results to avoid a delay in the diagnosis of breast carcinoma.     

Sonographic evaluation of focal asymmetric density of the breast

The purpose of this study was to examine the role of sonography in the evaluation of a focal asymmetric density of the breast in patients who subsequently underwent biopsy for this finding. During a 30-month period, the clinical, sonographic, and pathologic findings were retrospectively reviewed in 36 women who underwent biopsy for a focal asymmetric density of the breast after mammographic and sonographic workup. Sonographic evaluation of a focal asymmetric density of the breast in 36 women demonstrated a solid mass in 15, a suspected complicated cyst in two, echogenic tissue in nine women, and no focal sonographic change in 10. Excisional biopsy of the focal asymmetric density revealed infiltrating ductal cancer in seven patients (19.4%: 7/36). Two of these seven patients with breast cancer had no focal abnormality at sonographic examination. Twenty-nine patients had benign pathologic findings. In this retrospective study, the negative predictive value of sonography for breast cancer in a patient with a focal asymmetric density undergoing biopsy was found to be 89.4% (17/19). Sonographic evaluation of a focal asymmetric density is helpful, particularly to identify an underlying mass. When sonography demonstrates echogenic tissue corresponding to the focal asymmetric density, a benign process is likely; however, absence of a corresponding focal finding does not exclude malignancy. Therefore, although the negative predictive value of sonography for breast cancer in a patient with a focal asymmetric density is high, biopsy is still indicated for this mammographic finding when it is new, enlarging, or palpable, even in the absence of a suspicious sonographic finding.     

Measures of divergence of oriented patterns for the detection of architectural distortion in prior mammograms

Purpose We propose a method for the detection of architectural distortion in prior mammograms of interval-cancer cases based on the expected orientation of breast tissue patterns in mammograms. Methods The expected orientation of the breast tissue at each pixel was derived by using automatically detected landmarks including the breast boundary, the nipple, and the pectoral muscle (in mediolateral-oblique views). We hypothesize that the presence of architectural distortion changes the normal expected orientation of breast tissue patterns in a mammographic image. The angular deviation of the oriented structures in a given mammogram as compared to the expected orientation was analyzed to detect potential sites of architectural distortion using a measure of divergence of oriented patterns. Each potential site of architectural distortion was then characterized using measures of spicularity and angular dispersion specifically designed to represent spiculating patterns. The novel features for the characterization of spiculating patterns include an index of divergence of spicules computed from the intensity image and Gabor magnitude response using the Gabor angle response; radially weighted difference and angle-weighted difference (AWD) measures of the intensity, Gabor magnitude, and Gabor angle response; and AWD in the entropy of spicules computed from the intensity, Gabor magnitude, and Gabor angle response. Results Using the newly proposed features with a database of 106 prior mammograms of 56 interval-cancer cases and 52 mammograms of 13 normal cases, through feature selection and pattern classification with an artificial neural network, an area under the receiver operating characteristic curve of 0.75 was obtained. Free-response receiver operating characteristic analysis indicated a sensitivity of 0.80 at 5.3 false positives (FPs) per patient. Combining the features proposed in the present paper with others described in our previous works led to significant improvement with a sensitivity of 0.80 at 3.7 FPs per patient. Conclusion The proposed methods can detect architectural distortion in prior mammograms taken 15 months (on the average) before clinical diagnosis of breast cancer, but the FP rate needs to be reduced.     

超声BI—RADS分级标准指导判断乳腺肿块良恶性的价值

目的：探讨超声乳腺影像报告和数据系统（Breastimagingreportinganddatasystem,BI—RADS）分级诊断标准在乳腺肿块良恶性鉴别中的应用价值。方法：选取行乳腺彩色多普勒超声检查的患者93例（共121个乳腺肿块）。采用BI—RADS分级诊断标准对乳腺肿块进行分级,根据超声表现对肿块做出良恶性的判断。比较良恶性肿块超声图像的差异并分析BI—RADS分级诊断标准对肿块良恶性鉴别的性能。结果：乳腺恶性肿块形态不规则,边界不清晰、内部呈低回声、周围组织不完整、血供丰富以及有腋下淋巴结肿大,与乳腺良性肿块相比,差异具有统计学意义（P〈0．05）。除BI—RADS分级诊断标准中4B级肿块的阳性预测值稍低外（50％）,其余级别乳腺肿块对良恶性鉴别的敏感度、特异度、阳性预测值及阴性预测值均较好（66．67％。100％）。特别是1、2、3级及5级的肿块,其良恶性判断结果与病理结果完全一致。结论：乳腺恶性肿块形态不规则,边界不清晰、内部呈低回声、周围组织不完整、血供丰富以及有腋下淋巴结肿大。超声BI—RADS分级诊断标准具有较好的鉴别乳腺肿块良恶性的能力。     

乳腺黏液癌的影像学特征分析

目的 评价乳腺黏液癌的超声、X线等影像学特征及其与病理组织类型的相关性.方法 回顾性分析21例经手术病理证实的乳腺黏液癌患者(22个病灶)的超声、X线特征及与病理组织类型之间的关系.结果 病理组织学分类包括14个单纯型(6个富细胞型,8个少细胞型)和8个混合型病灶.超声:所有病例均存在实性肿块,85.71％(12/14)的单纯型肿块境界清晰,回声等或略低于皮下脂肪,92.86％(13/14)的单纯型病灶后方回声增强;75.00％(6/8)的混合型和14.29％(2/14)的单纯型肿块边界较模糊并细小毛刺,内部回声较脂肪回声低.超声和X线片术前怀疑恶性的比例均为63.64％(14/22).恶性X线表现包括肿块(10个)、局限性不对称致密影(2个)、结构扭曲并恶性钙化和单纯不定性钙化(各1个).肿块主要为高密度,单纯型边界清楚或呈浅分叶状,混合型边界不规则和毛刺改变.81.82％(18/22)的病灶被超声或X线之一疑诊恶性,45.45％(10/22)的病灶术前超声和X线均疑为恶性.结论 乳腺黏液癌尤其单纯型影像学特征不典型,超声和X线诊断均可能诊断为良性病变;肿块边缘特征是鉴别良恶性的重要依据,混合型肿块较单纯型更具有浸润性特征;超声和X线联合诊断有利于避免误诊,两者之一怀疑恶性时,即应行穿刺活检以明确诊断.     

Real-time elastography--an advanced method of ultrasound: First results in 108 patients with breast lesions.

OBJECTIVES: To evaluate whether real-time elastography, a new, non-invasive method for the diagnosis of breast cancer, improves the differentiation and characterization of benign and malignant breast lesions. METHODS: Real-time elastography was carried out in 108 potential breast tumor patients with cytologically or histologically confirmed focal breast lesions (59 benign, 49 malignant; median age, 53.9 years; range, 16-84 years). Tumor and healthy tissue were differentiated by measurement of elasticity based on the correlation between tissue properties and elasticity modulus. Evaluation was performed using the three-dimensional (3D) finite element method, in which the information is color-coded and superimposed on the B-mode ultrasound image. A second observer evaluated the elastography images, in order to improve the objectivity of the method. The results of B-mode scan and elastography were compared with those of histology and previous sonographic findings. Sensitivities and specificities were calculated, taking histology as the gold standard. RESULTS: B-mode ultrasound had a sensitivity of 91.8% and a specificity of 78%, compared with sensitivities of 77.6% and 79.6% and specificities of 91.5% and 84.7%, respectively, for the two observers evaluating elastography. Agreement between B-mode ultrasound and elastography was good, yielding a weighted kappa of 0.67. CONCLUSIONS: Our initial clinical results suggest that real-time elastography improves the specificity of breast lesion diagnosis and is a promising new approach for the diagnosis of breast cancer. Elastography provides additional information for differentiating malignant BI-RADS (breast imaging reporting and data system) category IV lesions.     

基于分形特征序列的乳腺X线图像分类方法

目的表征乳腺图像中肿块部分纹理特征,通过纹理分析实现乳腺图像中肿块部分与正常腺体部分的分类。方法应用分形特征值表征乳腺图像纹理特征,利用多级分形特征提取法将乳腺图像分解成一系列细节图像,提取出多个分形特征值;利用分类精度、ROC曲线及曲线下面积(AUC)进行特征选择构建分形特征序列,最后应用支持向量机(SVM)方法进行分类。结果对60幅图像的可疑病变区域进行分形特征序列提取分析,SVM交叉验证分类精度达84.50%。结论基于分形维数的乳腺图像分类方法不仅能对肿块与正常腺体进行图像分类,还可有效表征乳腺图像的纹理信息,有助于提高乳腺肿块诊断的准确率。     

数字化乳腺X线摄影检出BI-RADS 3~5级微钙化病变的动态增强MR研究

目的探讨DCE-MRI对数字化乳腺X线摄影检出BI-RADS3-5级微钙化病变的诊断价值。方法44例数字化乳腺X线摄影发现BI-RADS3-5级微钙化的患者于活检前完成双乳DCE-MR检查,以术后病理诊断为金标准,分析BI-RADS3～5级微钙化病变的DCE-MRI特点。结果病理证实良性微钙化病变13例,恶性微钙化病变31例。38.46%(5/13)良性单纯微钙化病变在DCE-MRI上无强化,93.33%(14/15)恶性微钙化伴肿块或局限性致密影病变在DCE-MRI上有明显强化,良恶性微钙化病变在DCE-MRI上早期增强率之间差异有显著统计学意义。DCE-MRI对微钙化病变诊断的敏感性和特异性分别为96.77%和92.31%。结论良恶性微钙化病变的DCE-MRI表现有明显差异,DCE-MRI有助于微钙化病变的定性诊断。     

In vivo Breast Sound-Speed Imaging with Ultrasound Tomography

We discuss a bent-ray ultrasound tomography algorithm with total-variation (TV) regularization. We have applied this algorithm to 61 in vivo breast datasets collected with our in-house clinical prototype for imaging sound-speed distributions in the breast. Our analysis showed that TV regularization could preserve sharper lesion edges than the classic Tikhonov regularization. Furthermore, the image quality of our TV bent-ray sound-speed tomograms was superior to that of the straight-ray counterparts for all types of breasts within BI-RADS density categories 1 through 4. Our analysis showed that the improvements for average sharpness (in the unit of (m · s)⁻¹) of lesion edges in our TV bent-ray tomograms are between 2.1 to 3.4-fold compared with the straight ray tomograms. Reconstructed sound-speed tomograms illustrated that our algorithm could successfully image fatty and glandular tissues within the breast. We calculated the mean sound-speed values for fatty tissue and breast parenchyma as 1422 ± 9 m/s (mean ± SD) and1487 ± 21 m/s, respectively. Based on 32 lesions in a cohort of 61 patients, we also found that the mean sound-speed for malignant breast lesions (1548 ± 17 m/s) was higher, on average, than that of benign ones (1513 ± 27 m/s) (one-sided p < 0.001). These results suggest that, clinically, sound-speed tomograms can be used to assess breast density (and therefore, breast cancer risk), as well as detect and help differentiate breast lesions. Finally, our sound-speed tomograms may also be a useful tool to monitor the clinical response of breast cancer patients to neo-adjuvant chemotherapy. (E-mail: lic@karmanos.org)     

S-DetectTM分类技术诊断BI-RADS 4类乳腺肿块

目的 观察S-Detect^TM分类技术鉴别诊断BI-RADS 4类乳腺良恶性肿块的价值。方法 对94例经二维超声诊断为BI-RADS 4类乳腺肿块患者（共104个肿块）行S-Detect^TM分类技术检查，以手术或穿刺活检病理结果作为金标准，评价S-Detect^TM分类技术、BI-RADS分类及二者联合应用诊断乳腺BI-RADS 4类良恶性肿块的价值。结果 104个乳腺肿块，经病理确诊为良性41个、恶性63个。S-Detect^TM分类技术诊断乳腺BI-RADS 4a类乳腺肿块的敏感度（SE）66.67%，特异度（SP）89.29%、阳性预测值（PPV）57.14%、阴性预测值（NPV）92.59%；对乳腺BI-RADS 4b类肿块分别为90.91%、60.00%、88.24%及66.67%；对乳腺BI-RADS 4c类肿块分别为95.83%、66.67%、95.83%及66.67%。S-Detect^TM分类技术联合BI-RADS分类诊断乳腺肿块的SE、SP、准确率明显均高于单独运用（P均<0.05）。结论 S-Detect^TM分类技术判断乳腺BI-RADS 4a类良性肿块、BI-RADS 4b类及BI-RADS 4c类恶性肿块均有较高价值。S-Detect^TM分类技术联合BI-RADS分类可明显提高鉴别BI-RADS 4类乳腺良恶性肿块的效能。     

The role of mammography in male patients with breast symptoms

OBJECTIVE: To determine the contribution of mammography to the comprehensive clinical evaluation of men with breast symptoms. PATIENTS AND METHODS: We retrospectively reviewed the records of all men who underwent mammography between January 1, 2001, and December 31, 2004, at the Mayo Clinic In Jacksonville, Fla. Medical history, mammographic findings, and breast cancer diagnoses were assessed. RESULTS: A total of 198 men had 212 mammograms. Nine mammograms (from 9 different men) (4%) showed suspicious findings. Eight men underwent biopsy, which yielded a breast cancer diagnosis in 2 (1%). Of the 212 mammograms, 203 (96%) showed benign findings, including gynecomastia on 132 (62%). One patient with a benign-appearing mammogram later underwent breast biopsy, and malignant disease was diagnosed. All the men with breast cancer had a dominant mass on clinical examination and other findings suggestive of breast cancer. Of the 132 mammograms showing gynecomastia, 110 (83%) were from men who had taken predisposing medications or who had predisposing medical conditions. CONCLUSIONS: Mammography added little information to the initial patient evaluation. Breast cancer may be suspected by the presence of a dominant mass. Gynecomastia can be predicted on the basis of the patient's symptoms or preexisting condition. Patients with suspicious findings on examination warrant appropriate clinical management regardless of mammographic findings. Mammography in men may be of benefit only for image guidance of percutaneous biopsy of a suspicious mass.